R: Convert variable (possibly multiple choice question) to...

as.dichotomy {expss}

R Documentation

Convert variable (possibly multiple choice question) to data.frame/matrix of dummy variables.

Description

This function converts variable/multiple response variable (vector/matrix/data.frame) with category encoding into data.frame/matrix with dichotomy encoding (0/1) suited for most statistical analysis, e. g. clustering, factor analysis, linear regression and so on.

as.dichotomy returns data.frame of class 'dichotomy' with 0, 1 and possibly NA.
dummy returns matrix of class 'dichotomy' with 0, 1 and possibly NA.
dummy1 drops last column in dichotomy matrix. It is useful in many cases because any column of such matrix usually is linear combinations of other columns.

Usage

as.dichotomy(
  x,
  prefix = "v",
  keep_unused = FALSE,
  use_na = TRUE,
  keep_values = NULL,
  keep_labels = NULL,
  drop_values = NULL,
  drop_labels = NULL,
  presence = 1,
  absence = 0
)

dummy(
  x,
  keep_unused = FALSE,
  use_na = TRUE,
  keep_values = NULL,
  keep_labels = NULL,
  drop_values = NULL,
  drop_labels = NULL,
  presence = 1,
  absence = 0
)

dummy1(
  x,
  keep_unused = FALSE,
  use_na = TRUE,
  keep_values = NULL,
  keep_labels = NULL,
  drop_values = NULL,
  drop_labels = NULL,
  presence = 1,
  absence = 0
)

is.dichotomy(x)

Arguments

`x`	vector/factor/matrix/data.frame.
`prefix`	character. By default "v".
`keep_unused`	Logical. Should we create columns for unused value labels/factor levels? FALSE by default.
`use_na`	Logical. Should we use NA for rows with all NA or use 0's instead. TRUE by default.
`keep_values`	Numeric/character. Values that should be kept. By default all values will be kept.
`keep_labels`	Numeric/character. Labels/levels that should be kept. By default all labels/levels will be kept.
`drop_values`	Numeric/character. Values that should be dropped. By default all values will be kept. Ignored if keep_values/keep_labels are provided.
`drop_labels`	Numeric/character. Labels/levels that should be dropped. By default all labels/levels will be kept. Ignored if keep_values/keep_labels are provided.
`presence`	numeric value which will code presence of the level. By default it is 1. Note that all tables functions need that `presence` and `absence` will be 1 and 0.
`absence`	numeric value which will code absence of the level. By default it is 0. Note that all tables functions need that `presence` and `absence` will be 1 and 0.

Value

as.dichotomy returns data.frame of class dichotomy with 0,1. Columns of this data.frame have variable labels according to value labels of original data. If label doesn't exist for particular value then this value will be used as variable label. dummy returns matrix of class dichotomy. Column names of this matrix are value labels of original data.

Examples

data.table::setDTthreads(2)
# toy example
# brands - multiple response question
# Which brands do you use during last three months? 
set.seed(123)
brands = as.sheet(t(replicate(20,sample(c(1:5,NA),4,replace = FALSE))))
# score - evaluation of tested product
score = sample(-1:1,20,replace = TRUE)
var_lab(brands) = "Used brands"
val_lab(brands) = autonum("
                              Brand A
                              Brand B
                              Brand C
                              Brand D
                              Brand E
                              ")

var_lab(score) = "Evaluation of tested brand"
val_lab(score) = make_labels("
                             -1 Dislike it
                              0 So-so
                              1 Like it    
                             ")

cro_cpct(as.dichotomy(brands), score)
# the same as
cro_cpct(mrset(brands), score)

# customer segmentation by used brands
kmeans(dummy(brands), 3)

# model of influence of used brands on evaluation of tested product 
summary(lm(score ~ dummy(brands)))

# prefixed data.frame 
as.dichotomy(brands, prefix = "brand_")